from pickle import UnpicklingError

import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict

from ..utils import logging


A						=			logging.get_logger(__name__)


def 	lowerCamelCase					(	UpperCamelCase     :				str			,       UpperCamelCase     :				List[Any]							)   ->    Any:
		try:
				with open(UpperCamelCase			,       'rb'							) as flax_state_f:
						_lowerCamelCase				   =  from_bytes(UpperCamelCase			,       flax_state_f.read()							)
		except UnpicklingError as e:
				try:
						with open(UpperCamelCase							) as f:
								if f.read().startswith('version'							):
										raise OSError(
										    'You seem to have cloned a repository without having git-lfs installed. Please'
										    ' install git-lfs and run `git lfs install` followed by `git lfs pull` in the'
										    ' folder you cloned.'							)
								else:
										raise ValueError from e
				except (UnicodeDecodeError, ValueError):
						raise EnvironmentError(F"""Unable to convert {model_file} to Flax deserializable object. """							)

		return load_flax_weights_in_pytorch_model(UpperCamelCase			,       UpperCamelCase							)


def 	lowerCamelCase					(	UpperCamelCase     :				List[Any]			,       UpperCamelCase     :				Tuple							)   ->    Optional[Any]:

		try:
				import torch  # noqa: F401
		except ImportError:
				logger.error(
				    'Loading Flax weights in PyTorch requires both PyTorch and Flax to be installed. Please see'
				    ' https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation'
				    ' instructions.'							)
				raise

		# check if we have bf16 weights
		_lowerCamelCase				   =  flatten_dict(jax.tree_util.tree_map(lambda UpperCamelCase							: x.dtype == jnp.bfloataa			,       UpperCamelCase							)							).values()
		if any(UpperCamelCase							):
				# convert all weights to fp32 if they are bf16 since torch.from_numpy can-not handle bf16

				# and bf16 is not fully supported in PT yet.
				logger.warning(
				    'Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` '
				    'before loading those in PyTorch model.'							)
				_lowerCamelCase				   =  jax.tree_util.tree_map(
				    lambda UpperCamelCase							: params.astype(np.floataa							) if params.dtype == jnp.bfloataa else params			,       UpperCamelCase							)

		_lowerCamelCase				   =  ''

		_lowerCamelCase				   =  flatten_dict(UpperCamelCase			,       sep='.'							)
		_lowerCamelCase				   =  pt_model.state_dict()

		# keep track of unexpected & missing keys
		_lowerCamelCase				   =  []
		_lowerCamelCase				   =  set(pt_model_dict.keys()							)

		for flax_key_tuple, flax_tensor in flax_state_dict.items():
				_lowerCamelCase				   =  flax_key_tuple.split('.'							)

				if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
						_lowerCamelCase				   =  flax_key_tuple_array[:-1] + ['weight']
						_lowerCamelCase				   =  jnp.transpose(UpperCamelCase			,       (3, 2, 0, 1)							)
				elif flax_key_tuple_array[-1] == "kernel":
						_lowerCamelCase				   =  flax_key_tuple_array[:-1] + ['weight']
						_lowerCamelCase				   =  flax_tensor.T
				elif flax_key_tuple_array[-1] == "scale":
						_lowerCamelCase				   =  flax_key_tuple_array[:-1] + ['weight']

				if "time_embedding" not in flax_key_tuple_array:
						for i, flax_key_tuple_string in enumerate(UpperCamelCase							):
								_lowerCamelCase				   =  (
								    flax_key_tuple_string.replace('_0'			,       '.0'							)
								    .replace('_1'			,       '.1'							)
								    .replace('_2'			,       '.2'							)
								    .replace('_3'			,       '.3'							)
								    .replace('_4'			,       '.4'							)
								    .replace('_5'			,       '.5'							)
								    .replace('_6'			,       '.6'							)
								    .replace('_7'			,       '.7'							)
								    .replace('_8'			,       '.8'							)
								    .replace('_9'			,       '.9'							)
								)

				_lowerCamelCase				   =  '.'.join(UpperCamelCase							)

				if flax_key in pt_model_dict:
						if flax_tensor.shape != pt_model_dict[flax_key].shape:
								raise ValueError(
								    F"""Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected """
								    F"""to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}."""							)
						else:
								# add weight to pytorch dict
								_lowerCamelCase				   =  np.asarray(UpperCamelCase							) if not isinstance(UpperCamelCase			,       np.ndarray							) else flax_tensor
								_lowerCamelCase				   =  torch.from_numpy(UpperCamelCase							)
								# remove from missing keys
								missing_keys.remove(UpperCamelCase							)
				else:
						# weight is not expected by PyTorch model
						unexpected_keys.append(UpperCamelCase							)

		pt_model.load_state_dict(UpperCamelCase							)

		# re-transform missing_keys to list
		_lowerCamelCase				   =  list(UpperCamelCase							)

		if len(UpperCamelCase							) > 0:
				logger.warning(
				    'Some weights of the Flax model were not used when initializing the PyTorch model'
				    F""" {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing"""
				    F""" {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture"""
				    ' (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This'
				    F""" IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect"""
				    ' to be exactly identical (e.g. initializing a BertForSequenceClassification model from a'
				    ' FlaxBertForSequenceClassification model).'							)
		if len(UpperCamelCase							) > 0:
				logger.warning(
				    F"""Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly"""
				    F""" initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to"""
				    ' use it for predictions and inference.'							)

		return pt_model
import argparse
import math
import os
from copy import deepcopy

import torch
from audio_diffusion.models import DiffusionAttnUnetaD
from diffusion import sampling
from torch import nn

from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel


A						=			{
    'gwf-440k': {
        'url': 'https://model-server.zqevans2.workers.dev/gwf-440k.ckpt',
        'sample_rate': 4_8_0_0_0,
        'sample_size': 6_5_5_3_6,
    },
    'jmann-small-190k': {
        'url': 'https://model-server.zqevans2.workers.dev/jmann-small-190k.ckpt',
        'sample_rate': 4_8_0_0_0,
        'sample_size': 6_5_5_3_6,
    },
    'jmann-large-580k': {
        'url': 'https://model-server.zqevans2.workers.dev/jmann-large-580k.ckpt',
        'sample_rate': 4_8_0_0_0,
        'sample_size': 1_3_1_0_7_2,
    },
    'maestro-uncond-150k': {
        'url': 'https://model-server.zqevans2.workers.dev/maestro-uncond-150k.ckpt',
        'sample_rate': 1_6_0_0_0,
        'sample_size': 6_5_5_3_6,
    },
    'unlocked-uncond-250k': {
        'url': 'https://model-server.zqevans2.workers.dev/unlocked-uncond-250k.ckpt',
        'sample_rate': 1_6_0_0_0,
        'sample_size': 6_5_5_3_6,
    },
    'honk-140k': {
        'url': 'https://model-server.zqevans2.workers.dev/honk-140k.ckpt',
        'sample_rate': 1_6_0_0_0,
        'sample_size': 6_5_5_3_6,
    },
}


def 	lowerCamelCase					(	UpperCamelCase     :				Optional[int]			,       UpperCamelCase     :				Union[str, Any]							)   ->    Optional[Any]:
		return torch.atana(UpperCamelCase			,       UpperCamelCase							) / math.pi * 2


def 	lowerCamelCase					(	UpperCamelCase     :				str							)   ->    Union[str, Any]:
		_lowerCamelCase				   =  torch.sin(t * math.pi / 2							) ** 2
		_lowerCamelCase				   =  (1 - sigma**2) ** 0.5
		return alpha_sigma_to_t(UpperCamelCase			,       UpperCamelCase							)





class     lowerCAmelCase__     (				SCREAMING_SNAKE_CASE_			):


		'''simple docstring'''






		pass





class     lowerCAmelCase__     (				nn.Module			):


		'''simple docstring'''






		def __init__(       self     :     Dict     ,  snake_case__     :     Any		)      ->	Optional[Any]:
				super().__init__()

				_lowerCamelCase				   =  DiffusionAttnUnetaD(snake_case__     ,  n_attn_layers=4		)
				_lowerCamelCase				   =  deepcopy(self.diffusion		)
				_lowerCamelCase				   =  torch.quasirandom.SobolEngine(1     ,  scramble=snake_case__		)



def 	lowerCamelCase					(	UpperCamelCase     :				List[Any]							)   ->    List[str]:
		_lowerCamelCase				   =  MODELS_MAP[model_name]['url']
		os.system(F"""wget {url} ./"""							)

		return F"""./{model_name}.ckpt"""


A						=			{
    '1': 'resnets.0',
    '2': 'attentions.0',
    '3': 'resnets.1',
    '4': 'attentions.1',
    '5': 'resnets.2',
    '6': 'attentions.2',
}
A						=			{
    '8': 'resnets.0',
    '9': 'attentions.0',
    '10': 'resnets.1',
    '11': 'attentions.1',
    '12': 'resnets.2',
    '13': 'attentions.2',
}
A						=			{
    '1': 'resnets.0',
    '2': 'attentions.0',
    '3': 'resnets.1',
    '4': 'attentions.1',
    '5': 'resnets.2',
    '6': 'attentions.2',
    '8': 'resnets.3',
    '9': 'attentions.3',
    '10': 'resnets.4',
    '11': 'attentions.4',
    '12': 'resnets.5',
    '13': 'attentions.5',
}
A						=			{
    '0': 'resnets.0',
    '1': 'resnets.1',
    '2': 'resnets.2',
    '4': 'resnets.0',
    '5': 'resnets.1',
    '6': 'resnets.2',
}

A						=			{
    'skip': 'conv_skip',
    'main.0': 'conv_1',
    'main.1': 'group_norm_1',
    'main.3': 'conv_2',
    'main.4': 'group_norm_2',
}

A						=			{
    'norm': 'group_norm',
    'qkv_proj': ['query', 'key', 'value'],
    'out_proj': ['proj_attn'],
}


def 	lowerCamelCase					(	UpperCamelCase     :				Tuple							)   ->    int:
		if name.startswith('skip'							):
				return name.replace('skip'			,       RES_CONV_MAP['skip']							)

		# name has to be of format main.{digit}
		if not name.startswith('main.'							):
				raise ValueError(F"""ResConvBlock error with {name}"""							)

		return name.replace(name[:6]			,       RES_CONV_MAP[name[:6]]							)


def 	lowerCamelCase					(	UpperCamelCase     :				Optional[Any]							)   ->    Tuple:
		for key, value in ATTN_MAP.items():
				if name.startswith(UpperCamelCase							) and not isinstance(UpperCamelCase			,       UpperCamelCase							):
						return name.replace(UpperCamelCase			,       UpperCamelCase							)
				elif name.startswith(UpperCamelCase							):
						return [name.replace(UpperCamelCase			,       UpperCamelCase							) for v in value]
		raise ValueError(F"""Attn error with {name}"""							)


def 	lowerCamelCase					(	UpperCamelCase     :				Any			,       UpperCamelCase     :				int=13							)   ->    Optional[int]:
		_lowerCamelCase				   =  input_string

		if string.split('.'							)[0] == "timestep_embed":
				return string.replace('timestep_embed'			,       'time_proj'							)

		_lowerCamelCase				   =  0
		if string.startswith('net.3.'							):
				depth += 1
				_lowerCamelCase				   =  string[6:]
		elif string.startswith('net.'							):
				_lowerCamelCase				   =  string[4:]

		while string.startswith('main.7.'							):
				depth += 1
				_lowerCamelCase				   =  string[7:]

		if string.startswith('main.'							):
				_lowerCamelCase				   =  string[5:]

		# mid block
		if string[:2].isdigit():
				_lowerCamelCase				   =  string[:2]
				_lowerCamelCase				   =  string[2:]
		else:
				_lowerCamelCase				   =  string[0]
				_lowerCamelCase				   =  string[1:]

		if depth == max_depth:
				_lowerCamelCase				   =  MID_NUM_TO_LAYER[layer_num]
				_lowerCamelCase				   =  'mid_block'
		elif depth > 0 and int(UpperCamelCase							) < 7:
				_lowerCamelCase				   =  DOWN_NUM_TO_LAYER[layer_num]
				_lowerCamelCase				   =  F"""down_blocks.{depth}"""
		elif depth > 0 and int(UpperCamelCase							) > 7:
				_lowerCamelCase				   =  UP_NUM_TO_LAYER[layer_num]
				_lowerCamelCase				   =  F"""up_blocks.{max_depth - depth - 1}"""
		elif depth == 0:
				_lowerCamelCase				   =  DEPTH_0_TO_LAYER[layer_num]
				_lowerCamelCase				   =  F"""up_blocks.{max_depth - 1}""" if int(UpperCamelCase							) > 3 else 'down_blocks.0'

		if not string_left.startswith('.'							):
				raise ValueError(F"""Naming error with {input_string} and string_left: {string_left}."""							)

		_lowerCamelCase				   =  string_left[1:]

		if "resnets" in new_layer:
				_lowerCamelCase				   =  convert_resconv_naming(UpperCamelCase							)
		elif "attentions" in new_layer:
				_lowerCamelCase				   =  convert_attn_naming(UpperCamelCase							)
				_lowerCamelCase				   =  new_string_left

		if not isinstance(UpperCamelCase			,       UpperCamelCase							):
				_lowerCamelCase				   =  prefix + '.' + new_layer + '.' + string_left
		else:
				_lowerCamelCase				   =  [prefix + '.' + new_layer + '.' + s for s in string_left]
		return new_string


def 	lowerCamelCase					(	UpperCamelCase     :				List[Any]							)   ->    int:
		_lowerCamelCase				   =  {}
		for k, v in state_dict.items():
				if k.endswith('kernel'							):
						# up- and downsample layers, don't have trainable weights
						continue

				_lowerCamelCase				   =  rename(UpperCamelCase							)

				# check if we need to transform from Conv => Linear for attention
				if isinstance(UpperCamelCase			,       UpperCamelCase							):
						_lowerCamelCase				   =  transform_conv_attns(UpperCamelCase			,       UpperCamelCase			,       UpperCamelCase							)
				else:
						_lowerCamelCase				   =  v

		return new_state_dict


def 	lowerCamelCase					(	UpperCamelCase     :				List[str]			,       UpperCamelCase     :				Tuple			,       UpperCamelCase     :				Dict							)   ->    Optional[Any]:
		if len(UpperCamelCase							) == 1:
				if len(v.shape							) == 3:
						# weight
						_lowerCamelCase				   =  v[:, :, 0]
				else:
						# bias
						_lowerCamelCase				   =  v
		else:
				# qkv matrices
				_lowerCamelCase				   =  v.shape[0]
				_lowerCamelCase				   =  trippled_shape // 3
				for i in range(3							):
						if len(v.shape							) == 3:
								_lowerCamelCase				   =  v[i * single_shape : (i + 1) * single_shape, :, 0]
						else:
								_lowerCamelCase				   =  v[i * single_shape : (i + 1) * single_shape]
		return new_state_dict


def 	lowerCamelCase					(	UpperCamelCase     :				Any							)   ->    Optional[Any]:
		_lowerCamelCase				   =  torch.device('cuda' if torch.cuda.is_available() else 'cpu'							)

		_lowerCamelCase				   =  args.model_path.split('/'							)[-1].split('.'							)[0]
		if not os.path.isfile(args.model_path							):
				assert (
				    model_name == args.model_path
				), F"""Make sure to provide one of the official model names {MODELS_MAP.keys()}"""
				_lowerCamelCase				   =  download(UpperCamelCase							)

		_lowerCamelCase				   =  MODELS_MAP[model_name]['sample_rate']
		_lowerCamelCase				   =  MODELS_MAP[model_name]['sample_size']

		_lowerCamelCase				   =  Object()
		_lowerCamelCase				   =  sample_size
		_lowerCamelCase				   =  sample_rate
		_lowerCamelCase				   =  0

		_lowerCamelCase				   =  UNetaDModel(sample_size=UpperCamelCase			,       sample_rate=UpperCamelCase							)
		_lowerCamelCase				   =  diffusers_model.state_dict()

		_lowerCamelCase				   =  DiffusionUncond(UpperCamelCase							)
		orig_model.load_state_dict(torch.load(args.model_path			,       map_location=UpperCamelCase							)['state_dict']							)
		_lowerCamelCase				   =  orig_model.diffusion_ema.eval()
		_lowerCamelCase				   =  orig_model.state_dict()
		_lowerCamelCase				   =  rename_orig_weights(UpperCamelCase							)

		_lowerCamelCase				   =  set(renamed_state_dict.keys()							) - set(diffusers_state_dict.keys()							)
		_lowerCamelCase				   =  set(diffusers_state_dict.keys()							) - set(renamed_state_dict.keys()							)

		assert len(UpperCamelCase							) == 0, F"""Problem with {renamed_minus_diffusers}"""
		assert all(k.endswith('kernel'							) for k in list(UpperCamelCase							)							), F"""Problem with {diffusers_minus_renamed}"""

		for key, value in renamed_state_dict.items():
				assert (
				    diffusers_state_dict[key].squeeze().shape == value.squeeze().shape
				), F"""Shape for {key} doesn't match. Diffusers: {diffusers_state_dict[key].shape} vs. {value.shape}"""
				if key == "time_proj.weight":
						_lowerCamelCase				   =  value.squeeze()

				_lowerCamelCase				   =  value

		diffusers_model.load_state_dict(UpperCamelCase							)

		_lowerCamelCase				   =  1_00
		_lowerCamelCase				   =  33

		_lowerCamelCase				   =  IPNDMScheduler(num_train_timesteps=UpperCamelCase							)

		_lowerCamelCase				   =  torch.manual_seed(UpperCamelCase							)
		_lowerCamelCase				   =  torch.randn([1, 2, config.sample_size]			,       generator=UpperCamelCase							).to(UpperCamelCase							)

		_lowerCamelCase				   =  torch.linspace(1			,       0			,       steps + 1			,       device=UpperCamelCase							)[:-1]
		_lowerCamelCase				   =  get_crash_schedule(UpperCamelCase							)

		_lowerCamelCase				   =  DanceDiffusionPipeline(unet=UpperCamelCase			,       scheduler=UpperCamelCase							)

		_lowerCamelCase				   =  torch.manual_seed(33							)
		_lowerCamelCase				   =  pipe(num_inference_steps=UpperCamelCase			,       generator=UpperCamelCase							).audios

		_lowerCamelCase				   =  sampling.iplms_sample(UpperCamelCase			,       UpperCamelCase			,       UpperCamelCase			,       {}							)
		_lowerCamelCase				   =  generated.clamp(-1			,       1							)

		_lowerCamelCase				   =  (generated - audio).abs().sum()
		_lowerCamelCase				   =  (generated - audio).abs().max()

		if args.save:
				pipe.save_pretrained(args.checkpoint_path							)

		print('Diff sum'			,       UpperCamelCase							)
		print('Diff max'			,       UpperCamelCase							)

		assert diff_max < 1e-3, F"""Diff max: {diff_max} is too much :-/"""

		print(F"""Conversion for {model_name} successful!"""							)


if __name__ == "__main__":
				A						=			argparse.ArgumentParser()

				parser.add_argument('--model_path', default=None, type=str, required=True, help='Path to the model to convert.')
				parser.add_argument(
				    '--save', default=True, type=bool, required=False, help='Whether to save the converted model or not.'
				)
				parser.add_argument('--checkpoint_path', default=None, type=str, required=True, help='Path to the output model.')
				A						=			parser.parse_args()

				main(args)